L’ambiguïté du discours publics de l’entreprise : entre générosité et mensonge
Bibliographic record
Abstract
En quête d’une légitimité sociale, la grande entreprise entreprend des démarches de séduction auprès de l’opinion publique pour se bâtir une image de confiance. En même temps, pour contrer les critiques des groupes de pression et des activistes qui dénoncent ses comportements nuisibles à la société et à l’environnement, elle a recours à des stratégies de communication dont le but premier est de semer le doute dans l’esprit du public, sinon de le tromper. Cet article situe ce glissement de la générosité au pur mensonge, en présentant les dérives de la responsabilité sociale et de l’écoblanchiment jusqu’à la création de groupes de façade créés dans le seul but de jeter la confusion dans l’esprit du public.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.013 | 0.061 |
| Scholarly communication | 0.017 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".